Recovery of true scatter in blocked regions for blocker-based scatter correction of CBCT Scatter correction (SC) is critically important to mitigate problems of shading artifacts, reduced contrast and inaccurate CT numbers of con-beam computed tomography (CBCT) for accurate and precise radiation dose delivery for image-guided adaptive radiotherapy. Among numerous SC methods, the use of lead-strip blockers is low-cost and easy to implement, where the signal detected in the blocked region is deemed scatter under an ideal condition to estimate scatter in the unblocked region. A moving-blocker approach has been developed to estimate scatter and reconstruct the volume image simultaneously from a single CBCT scan and holds the additional advantages of 1) high accuracy potential with subject-dependent measurements; 2) simple lead strip design; and 3) substantial dose reduction benefit (~50%). Since the signal in the blocked region is not pure scatter, heuristic parameter adjustment (denoted as manually tuned scatter estimation, MTSE) has to be done to achieve satisfying SC performance. Moreover, MTSE is hard to optimize these parameters in a spatially variant way and could produce heterogeneous reconstruction results, where large CT number errors (>100 HU) in some regions could occur and were clinically unacceptable (> 2% dose error) for treatment planning. In this work, we propose to overcome the problems of MTSE using rigorous modeling of the blocker-based image acquisition and deconvolution based scatter estimation (DBSE). Our goal is to eliminate manual parameter tuning and to achieve acceptable accuracy of reconstructed CT numbers over all regions of interest for dose calculation (< 100 HU, i.e. < 2% dose error) and significantly improved soft tissue contrast for effective organ segmentation. This goal will be achieved by the following specific aims: 1) to model the detector response function and the penumbra effect, which contaminate the scatter signal in the blocked region with the primary signal in the unblocked region; 2) to develop deconvlution methods for DBSE (frequency-domain methods, maximum likelihood estimate, and constrained optimization); and 3) to validate the model and to evaluate DBSE using Monte Carlo simulation and existing data of physical phantoms using clinically relevant criteria. Successful completion of this research will greatly improve the reliability, robustness, and accuracy of the moving-blocker technology and other blocker-based methods for CBCT, which is an important step to translate them into clinic for accurate soft-tissue localization and dose calculation in adaptive radiotherapy.